CN112740281B

CN112740281B - Method and system for generating objects

Info

Publication number: CN112740281B
Application number: CN201980064760.XA
Authority: CN
Inventors: E·古迭尔冈萨雷斯; V·迭戈古铁雷斯; M·弗雷勒加尔西亚
Original assignee: Peridot Printing Co ltd; Hewlett Packard Development Co LP
Current assignee: Peridot Printing Co ltd
Priority date: 2019-01-31
Filing date: 2019-01-31
Publication date: 2025-07-29
Anticipated expiration: 2039-01-31
Also published as: EP3824442A4; EP3824442A1; CN112740281A; WO2020159509A1; US11731364B2; US20230339188A1; US20210181714A1

Abstract

In an example, a method includes receiving, at least one processor, object model data representing at least a portion of an object to be generated by an additive manufacturing apparatus by fusing build material within a manufacturing chamber. At least one of a plurality of different geometric compensation models to be applied to the object model data may be selected, wherein the geometric compensation model is used to determine geometric compensation to compensate for object deformations in additive manufacturing. Modified object generation operations based on object model data using the or each selected geometric compensation mode may be simulated and predicted attributes based on the object at the time of the or each simulation generation may be displayed.

Description

Method and system for generating objects

Background

Additive manufacturing techniques may generate three-dimensional objects by solidification of build material, for example, on a layer-by-layer basis. In an example of such a technique, the build material may be supplied in a layer-wise manner, and the solidification method may include heating the layer of build material to cause melting in the selected region. Among other techniques, chemical curing methods may be used.

Drawings

Non-limiting examples will now be described with reference to the accompanying drawings, in which:

FIG. 1 is a flow chart of an example method of predicting object properties;

FIGS. 2A and 2B illustrate examples of displays of additive manufacturing data;

FIG. 3 illustrates an example method of object generation;

FIGS. 4 and 5 are simplified schematic diagrams of an example apparatus for additive manufacturing, and

FIG. 6 is a simplified schematic diagram of an example machine-readable medium associated with a processor.

Detailed Description

Additive manufacturing techniques may generate three-dimensional objects by solidification of build material. In some examples, the build material is a powdered particulate material, which may be, for example, a plastic, ceramic, or metal powder, and the nature of the generated object may depend on the type of build material and the type of curing mechanism used. In some examples, the powder may be formed from or include short fibers, which may have been cut to short lengths, for example, from long strands (strands) or wires of material. Build material may be deposited on, for example, a print bed (print bed) and processed layer by layer, for example, within a fabrication chamber. According to one example, a suitable build material may be a PA12 build material commercially known as V1R10A "HP PA12" available from HP Inc.

In some examples, selective solidification is achieved using heat, such as by directional application of energy, such as using a laser or an electron beam, which results in solidification of the build material where directional energy is applied. In other examples, at least one printing agent (PRINT AGENT) may be selectively applied to the build material and may be a liquid when applied. For example, flux (also referred to as "coalescing agent" or "coalescing agent") may be selectively distributed over portions of the layer of build material in a pattern derived from data representing a slice of the three-dimensional object to be generated (which may be generated, for example, from structural design data). The flux may have a composition that absorbs energy such that when energy (e.g., heat) is applied to the layers, the build material heats, coalesces, and solidifies upon cooling to form a slice of the three-dimensional object according to the pattern. In other examples, coalescing may be achieved in some other manner.

According to one example, a suitable flux may be a flux formulation including an ink type formulation such as carbon black, such as the commercially known as V1Q60A "HP flux" available from HP Inc. In examples, such a flux may include any absorber or any combination of infrared light absorbers (absorbers), near infrared light absorbers, visible light absorbers, and UV light absorbers. Examples of printing agents that include visible light enhancers are dye-based colored inks and pigment-based colored inks, such as inks commercially known as CE039A and CE042A available from HP Inc.

In addition to the fusing agent, in some examples, the printing agent may include a coalescing modifier (coalescence modifier agent) that acts to modify the effect of the fusing agent, for example by reducing or increasing coalescence, or to help create a particular finish or appearance of the object, and such an agent may therefore be referred to as a refiner (DETAILING AGENT). In some examples, refiners may be used near the edge surface of the object being printed. According to one example, a suitable refiner may be a formulation commercially known as V1Q61A "HP refiner" available from HP Inc. A coloring agent (coloring agent), for example, comprising a dye or colorant, may be used as a flux or coalescing modifier in some examples, and/or as a printing agent to provide a particular color of the object.

As described above, the additive manufacturing system can generate objects based on structural design data. This may involve a designer generating a three-dimensional model of the object to be generated, for example using a Computer Aided Design (CAD) application. The model may define a solid (solid) portion of the object. To generate a three-dimensional object from a model using an additive manufacturing system, model data may be processed to generate slices of parallel planes of the model. Each slice may define a portion of a respective layer of build material to be solidified or agglomerated by the additive manufacturing system.

FIG. 1 is an example of a method, which may include a computer-implemented method of generating (at least part of) a simulation(s) of an object generation operation in order to predict object properties.

The simulated object generation operations may be based on different modifications of the object model data. For example, such modification of the object model data may be used to apply geometric compensation to compensate for expected deviations from the expected dimensions when generating the object.

For example, it may be the case that when an object is generated in a process that includes heat, additional build material may adhere to the object at the time of generation. In one example, the flux may be associated with an area of the layer intended to fuse. However, when energy is supplied, the build material of adjacent regions may heat up and fuse to the exterior of the object (in some examples, completely or partially melt, or adhere as powder to the melted build material). Thus, the size of the object(s) may be larger than the area where the flux is applied. To compensate for this effect, i.e. in case the intended object may tend to "grow" in this way during manufacture, the object volume described in the object model data may be reduced to compensate for this growth. The reduction of the volume may be defined in a geometric compensation/transformation model.

In other examples, the object after the object generation may be smaller than specified. For example, some build material used to generate an object may shrink when cooled. Thus, the geometric compensation/transformation model may specify how the object volume should be increased to compensate for the decrease.

The particular object may be subject to mechanisms that result in growth and shrinkage, and the actual compensation to be applied may be determined by considering the different degrees to which the object may be affected by such a process, or may be affected by the different degrees to which the object may be affected by such a process.

In some examples, the modification may be specified using scaling and/or offset parameters (e.g., scaling factors and/or offset factors). The scaling factor may be used to multiply all specified dimensions in the direction of at least one axis by a value that may be greater than 1 in order to increase the dimension(s), and may be less than 1 in order to decrease the dimension(s). The offset factor may specify the amount to be added or removed from the surface (or perimeter within a layer) of the object, for example, by specifying a distance or a defined number of sub-volumes or "voxels" (i.e., voxels). For example, a distance from the object surface measured in the normal direction may be specified, and the object may erode (eroded) or expand (dilated) (i.e., expand or dilate) the distance.

According to at least some of the methods set forth herein, different geometric compensation models may be used to generate different simulations. For example, in some cases it has been shown that the position of an object within a manufacturing chamber may have an effect on the deformation of the object as it is generated. Thus, the position of the object can be used to determine the appropriate compensation. The first geometric compensation model may generate a simulation based on a compensation factor determined based on a position of an object in the fabrication chamber, wherein the position is characterized in a first manner. Another geometric compensation model may characterize the position in a different way and may use the same and/or different compensation factors.

In other examples, the geometric compensation model may generate a simulation based on compensation factors determined based on the volume of the object, as the larger object may be differently deformed than the smaller object. For example, bulkier objects tend to accumulate more heat in hot melt based additive manufacturing operations.

Another geometric compensation model may consider, for example, the surface area of the object, in some examples in combination with the volume. The surface area (as well as the combination of volume and surface area) can be used to determine how "solid" the object is. The amount of solid material in the object may be used to predict how the object may deform. For example, in hot melt additive manufacturing operations, more solid objects may tend to accumulate more heat than less solid objects.

In some examples, a combination of such factors may be considered in the model, and/or there may be more than one model in a class, e.g., independently generated models, resulting in different compensation values. Examples of models are discussed in more detail below.

The method of fig. 1 includes, at block 102, receiving, at least one processor, object model data representing at least a portion of at least one object to be generated by an additive manufacturing apparatus by fusing build material within a manufacturing chamber. In some examples, the fusing process may include a hot-melting process in which heat is applied. The object model data may include data representing at least a portion (in some examples, slices) of an object to be generated by the additive manufacturing apparatus by fusing the build material. The object model data may include, for example, a Computer Aided Design (CAD) model, and/or may be, for example, STereoLithographic (STL) data files. In some examples, the object model data may represent the object or object portion as a plurality of sub-volumes, where each sub-volume represents a region of the object that is individually addressable in the object generation. In some examples herein, the sub-volumes may be referred to as voxels, i.e., voxels. In some examples, the object model data may represent a printable arrangement of a plurality of objects to be generated by the additive manufacturing apparatus by fusing build material within the manufacturing chamber.

The method further includes, in block 104, selecting, using at least one processor, at least one of a plurality of different geometric compensation models to be applied to the object model data, wherein the geometric compensation models are used to determine geometric compensation to compensate for object deformations in additive manufacturing.

For example, the geometric compensation may include a parametric transformation, e.g., a geometric transformation such as at least one of an offset and a scaling factor. For example, the geometric compensation vector (vector) may specify components in the X and Y axes (e.g., as applied to a single slice of the object), or in other examples may specify components in the X, Y and Z axes.

In some examples, the geometric compensation may be defined using two or three scaling factors (each of two/three axes, which may be orthogonal) and/or two or three offset factors (each of two/three axes, which may be orthogonal). If no scaling is indicated on a given axis, the scaling factor associated with that axis may be set to 1, and if no offset is indicated on the given axis, the offset factor associated with that axis may be set to 0.

Taking as an example the scaling factor is specified in each of the three orthogonal axes, in some examples this may be specified as a vector with components in X, Y and Z directions, and may for example be specified as [ SF _x,SF_y,SF_z ]. This may, for example, place the object in its intended generation orientation, meaning that the "width" of the object will be scaled by SF _x, the "depth" of the object will be scaled by SF _y, and the "height" of the object will be scaled by SF _z (note that in practice, an object may be generated in any orientation, and thus the height of the object during generation may not correspond to the height of the object as oriented for its use after generation).

The geometric compensation model may include one or more predefined geometric compensations and/or may include information for deriving geometric compensation(s) applied to the object. For example, the model may specify expected input parameter(s) (such as object position, object volume, and the like) that will be provided to determine geometric compensation(s) applied to the object. For example, there may be a mapping between such input parameter(s) and geometric compensation(s), as set forth in more detail below.

Such a geometric compensation model may be determined, for example, by trial and error (real and error) over time and/or using machine learning techniques. In some examples, the geometric compensation model may be generated based on thermal analysis and/or material considerations, and the like.

One example of a geometric compensation model may include one or a set of scaling and/or offset parameters associated with a particular object generating device or type of object generating device. Parameters may be applied to all objects in the same manner (e.g., regardless of object size and/or placement).

In other examples, the geometric compensation model may allow geometric compensation derived or selected therefrom to be customized (tailored) for a particular intended-object-generation operation and/or object.

For example, the geometric compensation model may consider an expected position of the object in the manufacturing chamber. It has been noted that by taking into account the position of the object generation when determining the compensation, the dimensional accuracy can be significantly improved, and thus different compensation parameters can be applied to different object positions to improve the accuracy. Thus, such a geometric compensation model may include or provide compensation parameters that may be mapped to the expected position of the object.

For example, if an object is to be generated at a first location, that location may be mapped to a geometric compensation that includes one or more offset and/or scaling parameters. However, if the same object is to be generated at a second location, the second location may be mapped to a different geometric compensation including one or more different offset and/or scaling parameters. Thus, the particular geometric compensation applied may vary between different locations based on a predetermined mapping or the like.

In some examples, in at least one geometric compensation model, a location may be modeled as a single representative location. In one example, this may include a center point of the object generated in additive manufacturing when the object is in its intended position within the manufacturing chamber. This may include generating a virtual manufacturing chamber in which one or more virtual objects are arranged in positions that the object is expected to occupy when generated. In some examples, the location is the centroid of the object, but in another example/model it may be some other location, such as the center of a bounding box, i.e. the smallest cube, lowest coordinates or any other predetermined coordinates that can completely enclose the object.

In some examples, the at least one geometric compensation model may include a plurality of defined geometric compensation parameters (or parameter sets), each associated with a different location within the fabrication chamber. In such examples, the particular geometric compensation parameter(s) may be selected based on the expected object generation location. In some examples, the defined position may be associated with geometric compensation parameter(s), and geometric compensation parameter(s) to be applied at intermediate positions of such defined positions may be generated, for example, by interpolation or by selecting the closest defined position or the like.

In some examples, the geometric compensation model may specify offset(s) and/or scaling parameters to be applied to the voxel model of the object, where the parameters are selected based on the location of the center of the object. In such examples, the object model data may represent the object or object portion as a plurality of sub-volumes, where each sub-volume represents a region of the object that is individually addressable in the object generation. In such examples, the offset may be applied by adding voxels or eroding voxels from the object, and may be performed strictly in the x, y, and z directions. The resolution of such an operation is related to the resolution of the voxels. For example, a resolution of 600dpi allows for uniquely addressable regions of 42 by 42 microns in cross-section, and thus voxels may be defined as being associated with 42 by 42 micron regions. This means that adjustments can be made with a minimum resolution of 42 microns (or in some examples, 84 microns, as the offset can be applied symmetrically).

In another example, the parameter set(s) of the geometric compensation model may be specified in the context of a compensation "vector" to be applied to the mesh model. Scalar projection or Hadamard product (i.e., component-to-component multiplication) of the geometric compensation vector may be determined, for example, for each vertex of the model, such that each vertex may be shifted (shift) by reference to a vector determined based on the orientation of the vertex relative to a coordinate system (e.g., xyz coordinate system) having a defined origin (which may be, for example, the center of the object). In other examples, the facets may be shifted.

For example, the compensation vector may be specified as an offset vector having the form [ O _x,O_y,O_z ]. Vertices of a mesh model of an object may be defined, which in turn define edges and faces. The faces are defined at different angles to X, Y and the Z-axis, and the outward facing normal to the face may be determined by reference to coordinates used to define the vertex, so that the normal n to a particular face may be defined as NF _nx,NF_ny,NF_nz. The defined normals can then be used to determine the offset to be applied to each face, for example using a Hadamard product, i.e. a component multiplication of the form NF _nx*O_x,NF_ny*O_y,NF_nz*O_z. In some examples, this may result in a pattern of "holes" appearing, which in some cases may be sealed with a definition of new faces or the like.

In other examples, vertices and/or edges may be shifted in a similar manner, with their normals determined in an appropriate manner.

Using this process, each vertex, edge, and/or triangle of the mesh may be offset in a manner determined by its orientation in the model. This results in a modified virtual object, wherein the modification can be applied independent of size. By applying the offset to the mesh model instead of the voxel model, a greater resolution may be achieved. Furthermore, the model may be "continuously" adjusted over the entire object surface as the applied offset evolves with the applied angle.

In the above examples, examples of geometric compensation models are described in which a single representative coordinate, such as a centroid or a center of a bounding box surrounding an object, is used to indicate the position of the object. However, especially for larger objects, since different parts of the object may be in different areas of the manufacturing chamber with different compensation parameters, this may lead to a loss of information, which may take into account compensation parameters or values designed to compensate for deformations associated with only one of these areas. Thus, in at least some geometric compensation models, values associated with multiple locations may be considered and combined. For example, compensation values related to a location surrounded by a volume of the object may be combined. In other examples, additional values (e.g., those associated with locations outside the object volume but within a threshold distance of the perimeter of the object) may alternatively or additionally be included in the combination. In some examples, combining may include determining an average (which may include, in some examples, a weighted average, e.g., a position-related value outside of the volume to be occupied by the object given lower weights than those positions within the volume to be occupied by the object). In such an example, a combination of multiple parameter sets may be used to determine the parameter set to apply to the object.

In another example, different compensation parameters may be applied to different model portions. For example, a scaling factor associated with a vertex of the object model may be selected based on the position of the vertex. In those cases, the geometry of the portion may be deformed more severely than scaling the entire portion, e.g., parallelism between the faces may be disrupted. This may be achieved, for example, by applying a scaling to each vertex from the center of the object (or any other fixed point).

In other examples, characteristics of the object, such as consideration of the object volume, may be used as input parameters in the geometric compensation model. For example, a larger object may accumulate (accrue) more thermal energy than a smaller object, and thus may tend to accumulate more heat than a smaller object. Thus, cooling such objects takes more time than cooling objects that are not too bulky, which may lead to different deformations. In addition, due to the higher thermal level, it is possible that additional build material adheres to such objects. Thus, in one example, the first compensation model may include compensation factors associated with the object volume, while in other examples, no such compensation factors may be present, or different compensation factors may be used. Other geometric compensation models may, for example, include consideration of how many objects and/or the proximity of objects (e.g., in terms of "packing density (PACKING DENSITY)") will be generated in the manufacturing chamber.

In other examples, other object-generated parameter values (which may be object-generated parameter values configurable or selectable by a user or operator) may be considered. The parameter(s) may be any parameter that may have an impact on dimensional inaccuracy. For example, the parameter(s) may include any content or any combination of environmental conditions, object generation devices, object generation material compositions (which may include selection of types or compositions of build materials and/or printing agents), object cooling profiles (profiles), or printing modes. These may be specified, for example, by input to at least one processor. Thus, different geometric compensation models may relate to different devices, different printing modes, different cooling curves, or the like.

The geometric compensation model may be stored, for example, in a memory, for example, embodied as mapping resources, such as look-up tables and the like, related to the parameter(s) to locations, or through the use of algorithms or the like.

Block 106 includes using at least one processor and at least partially simulating an object generation operation based on modifications to the object model data using the or each selected geometric compensation model.

In some examples, this may include generating at least one simulated object having characteristics predicted for object(s) generated based on modifications according to a particular geometric compensation model. In other examples, this may include modeling object properties, such as at least one object size (i.e., in all examples, generation of the entire object may not be modeled). The simulation may be determined, for example, using a model of object deformation generated with respect to the object, using object model data modified by a particular geometric transformation model. Such an object deformation model may be determined in a similar manner as described for the geometric compensation model (and may be generated with such a geometric compensation model in some examples). For example, an object deformation model may be generated by generating a plurality of test objects and observing their deformations, and inferring the object deformation behavior therefrom. In some examples, the effects of parameters such as object position, object volume, object surface area, and the like, any content or any combination thereof, may be included in the object deformation model. Machine learning techniques may be used to generate the simulation. In some examples, the object deformation model may be generated based on analysis of thermal and/or material considerations and the like.

In some examples, the same object deformation model is used for the object model data modified using each of the plurality of geometric compensation models.

Block 108 includes displaying, using the at least one processor, predicted attributes of the object(s) as generated based on the or each simulation. In some examples, this may include displaying the image(s) of the object. In other examples, characteristics such as size may be displayed. These may be displayed in a graphical or graphical form so that the user may quickly compare information. The predicted attributes may, for example, include the expected object size on each of the three orthogonal axes. In another example, as shown in fig. 2A and 2B, the predicted attribute may include an indication of a proportional deviation of at least one object size based on a magnitude of the size (magnitide).

Different geometric compensation models can be optimized or customized for different desired results. Thus, the geometric compensation model may produce different compensations, which may be more efficient in one region of the object than in another region, for example, and/or which may lead to unintended effects in a given object, but not in another object (artifacts (artefact)). For example, some geometric transformations may close gaps or holes in a particular object that are intended to remain open, or may cause holes to appear.

It may be the case that for a given use case, some object sizes or characteristics (or indeed some objects in the case where the generation of multiple objects is modeled) are considered to have a higher priority than others. By modeling such object sizes/characteristics, verification may be made that objects modified using a particular compensation model may be expected as related to those particular size (s)/object(s), while there may be a greater degree of tolerance related to other size (s)/object(s).

In some examples, the compensation model may be generated based on the number of test objects being generated. In some examples, the test object may include multiple instances of the same or just a few underlying (underlying) object data models. While such a compensation model may perform well for testing an object, another object may be modified, for example, in a manner that introduces distortion to the object. In this case, applying compensation may reduce the accuracy of the object.

By simulating the effects of different compensation models, the occurrence of the generated object not within the expected parameters can be detected and minimized. This may, for example, allow identification of the most appropriate compensation model for a particular object and/or use case.

By displaying such predicted attributes (e.g., graphically), appropriate compensation for a given use case may be selected. This in turn may prevent physical experimentation (e.g., printing or generating one or more test objects), saving time, material, and energy.

In some examples, the selection of the geometric compensation model to be used in the object generation may be done automatically for a predetermined criterion. In other examples, the selection may be made by a user evaluating the characteristics. The user may be readily able to identify the attributes of a given use case.

Fig. 2A shows the measurement of multiple instances of the same object printed in a single manufacturing chamber in a single printing operation, each object occupying a different location in the manufacturing chamber. Each line links data points associated with a particular object, the data specifying a deviation in millimeters of a predetermined object size from an expected value at different points along the nominal distance of the object in the nominal direction.

Such measurements may be displayed in a user interface. In some examples of the user interface, one of the lines may be selected in isolation from the other lines. In some examples of user interfaces, each size may be checked separately. In fig. 2A, the data displayed is data of the Z axis.

FIG. 2A also shows a number of metrics, including:

EI (error index): root mean square of squared error (squared error) is normalized to the nominal value multiplied by a factor of 1000.

MOS (extended measure) is the average over the nominal value of the standard deviation of the deviation, multiplied by a fixed normalization factor.

ADE (average deviation): average of all deviations.

SVA (slope variability): standard deviation on the slope portion of the linear regression of deviation as a function of nominal value multiplied by a fixed normalization factor.

Fig. 2B shows simulated data assuming the same set of objects to which a particular geometric transformation model has been applied. Both dimensional deviations and the same metric have been modeled and predicted. These metrics are purely by way of example, and in other examples different metrics or different combinations of metrics may be used.

It may be noted that in general, the predicted objects will be closer to the expected size than the measured printed objects shown in fig. 2A, but some objects will be predicted to be associated with larger deviations than other objects. In the particular example for generating the data shown in fig. 2B, a model using the position of each object as an input parameter has been applied.

There may be selectable input parameters. For example, the location of an object may be modeled as a point location in one instance and in another instance in a manner that extends over a volume.

A graphical user interface may be displayed to the user that may appear similar to fig. 2B. In some examples, the user may be able to select between geometric compensation models by using selectable options (e.g., a drop down menu, a selectable list, or any other user interface). The predicted properties of the object(s) after modifying the object model data according to the selected geometric compensation model(s) may be displayed. In some examples, a confirmation selection may be generated for the object after its display (displayer). In other words, one of the geometric compensation models that may be used to generate the simulation may be selected (by the user via the user interface or automatically) for object generation, as now described with respect to fig. 3.

FIG. 3 is an example method of object generation, including, in block 302, selecting modifications to object model data for generating an object. This may be selected, for example, by user input (e.g., a user may indicate a model to be used to determine modification), or automatically, for example, by evaluating predicted attributes against predetermined data. Modifications may be selected based on the output of the method of fig. 1, for example, after a user has reviewed simulations of a desired size and/or has reviewed a set of such simulations for its consistency with a predetermined standard. Selecting the modification may include selecting a geometric transformation model for determining the geometric transformation, and applying the geometric transformation using the selected model to generate modified object data. The user may select the modification/geometric transformation model using a graphical user interface, for example, by using a drop down menu or the like.

Block 304 includes determining object generation instructions (or "print instructions") for generating the object. In some examples, the object generation instructions may specify an amount of printing agent to be applied to each of a plurality of locations on the layer of build material. For example, generating the object generation instructions may include determining "slices" that include modifying the virtual build volume of the virtual object(s) to which the modifications have been applied, and rasterizing (rasterising) the slices into pixels (or voxels, i.e., voxels). An amount of printing agent (or no printing agent) may be associated with each of the pixels/voxels. For example, if a pixel is associated with a region of build volume that is expected to solidify, object generation instructions may be generated to specify a corresponding region of build material to which flux should be applied in object generation. However, if the pixel is associated with an area intended to remain an uncured build volume, then object generation instructions may be generated to specify that no agent may be applied thereto or that a coalescing modifier such as a refiner may be applied thereto. Further, the amounts of such agents may be specified in the generated instructions, and may be determined based on, for example, thermal considerations and the like.

Block 306 includes generating an object based on the object generation instruction. For example, such objects may be generated layer by layer. For example, this may include forming a layer of build material, applying a printing agent, e.g., using at least one printing agent applicator, at a location specified in an object generation instruction for an object model slice corresponding to the layer using an "inkjet" liquid distribution technique, and applying energy, e.g., heat, to the layer. Some techniques allow for accurate placement of the printing agent on the build material, such as by applying the printing agent using a printhead that operates according to the inkjet principles of two-dimensional printing, in some examples the printhead may be controlled to apply the printing agent at a resolution of about 600dpi or 1200 dpi. Additional layers of build material may then be formed and the process repeated, for example, with object generation instructions for the next slice.

In this way, the object formed at one time may eventually be closer to the intended size. Furthermore, since the appropriate model is more likely to be selected than if the model were selected without simulation, the object will be more likely to conform to the expected parameters and meet the user and/or technical specifications. In this way, the generation of unsuitable objects and the resulting waste of material, time and energy may be reduced or prevented.

In some examples, the methods set forth herein may be combined with other methods of object model modification. For example, the modification function may be employed in the vicinity of small features or locally. Such erosion of small features may result in an unacceptable reduction in their size, destroying (obliterating) the features or making them too small to fuse or too delicate to survive the cleaning operation. For example, if the feature has a size of about 0.5mm, this may correspond to 12 voxels at 600 dpi. If three or four voxels are eroded from the sides of such a small feature, it will lose about 50% to 60% of its cross-section, reducing its size to less than 0.3mm. Such a feature may be too small to survive the cleaning operation. Thus, in some examples, other functions may be used to ensure that small features are preserved.

Fig. 4 shows an apparatus 400 comprising a processing circuit 402. The processing circuitry 402 includes a memory resource 404, a model modification module 406, and a simulation module 408.

At least in use of the apparatus 400, the memory resource 404 stores a plurality of geometric compensation models to determine geometric compensation to compensate for object deformations in additive manufacturing. For example, this may include mapping any content or any combination of resource(s), transform vector(s), compensation parameter(s), algorithm(s), or the like, as described above. The geometric compensation models may be intended to compensate for object deformations in additive manufacturing, wherein each geometric compensation model may specify or determine a compensation to be applied based on predetermined criteria, which may vary between models. In some examples, as described above, the model may relate the compensation to be applied to any of object placement locations, object volumes, object surface areas, or the like within the fabrication chamber. The geometric compensation model may have any of the features of the geometric compensation model described above. The memory resource 404 may also store object deformation models as described above.

The model modification module 406 determines a geometric transformation of the object to be generated using additive manufacturing based on the selected geometric compensation model in use of the apparatus 400 and modifies the object model data using the geometric transformation.

Simulation module 408 generates a simulation of the output of the additive manufacturing operation based on the modified object model data in use of apparatus 400. The simulation output may include a simulation object or any attribute thereof. For example, the simulation module 408 may perform the process described above with respect to fig. 1, and may generate a simulation of at least one predicted object property (e.g., size or the like), for example, based on the object deformation model.

Fig. 5 shows an additive manufacturing apparatus 500 that generates objects. Additive manufacturing apparatus 500 includes processing circuitry 502. The processing circuitry 502 includes the memory resource 404, model modification module 406, and simulation module 408 of fig. 4, and also includes a print instruction module 504 and a display module 506.

The print instruction module 504 determines, in use of the apparatus 500, print instructions for generating objects from the modified object model(s) generated by the data model modification module 406.

The display module 506 may include a screen or the like to display the predicted properties of the object based on the simulated additive manufacturing operation.

Additive manufacturing apparatus 500, in its use, generates objects in a plurality of layers (which may correspond to respective slices of an object model) according to print instructions. Additive manufacturing apparatus 500 may generate objects in a layer-by-layer manner, for example, by selectively curing portions of layers of build material. In some examples, selective curing may be achieved by selectively applying a printing agent, such as by using an "inkjet" liquid distribution technique, and applying energy, such as heat, to the layer. Additive manufacturing apparatus 500 may include additional components not shown herein, such as any of a manufacturing chamber, a print bed, a printhead(s) for distributing a printing agent, a build material distribution system for providing a layer of build material, an energy source such as a heat lamp (heat lamp), and the like, or any combination thereof.

The print instructions (or object generation instructions) generated by print instruction module 504 may control additive manufacturing apparatus 500 in its use to generate each of the multiple layers of objects. This may include, for example, area coverage(s) designated for a printing agent, such as a flux, colorant, refiner, and the like. In some examples, the object generation parameters are associated with an object model sub-volume. In some examples, other parameters may be specified, such as any of or any combination of heating temperature, build material selection, expectations of printing modes, and the like. In some examples, halftoning (halftoning) may be applied to the determined object generation parameters to determine where to place flux or the like. The control data may be specified in association with a sub-volume (e.g., voxels as described above). In some examples, the control data includes a print dose associated with the subvolume.

The processing circuitry 402, 502 or modules thereof may perform any of the blocks of fig. 1, or any of the blocks 302-304 of fig. 3.

Fig. 6 illustrates a tangible machine-readable medium 600 associated with a processor 602. The machine-readable medium 600 includes instructions 604 that, when executed by the processor 602, cause the processor 602 to perform tasks. In this example, instructions 604 include instructions 606 to cause processor 602 to generate a plurality of object simulations, each object simulation based on applying a different deformation compensation model to object model data representing at least a portion of an object to be generated by the additive manufacturing apparatus by fusing build material within a manufacturing chamber, and instructions 608 to cause processor 602 to generate display data including properties of the simulated object. For example, data similar to that discussed with respect to fig. 2B may be displayed. In some examples, instructions 604 include instructions that when executed cause processor 602 to accept selection of a deformation compensation model, and determine print instructions for generating an object.

The deformation compensation model may have any of the features of the geometric compensation model described above.

In some examples, the instructions, when executed, cause the processor 602 to perform any of the blocks of fig. 1 or any of the blocks 302-304 of fig. 3. In some examples, the instructions may cause the processor 602 to act as any portion of the processing circuitry 402, 502 of fig. 4 or 5.

Examples in this disclosure may be provided as methods, systems, or machine-readable instructions, such as software, hardware, firmware, or any combination thereof. Such machine-readable instructions may be included on a computer-readable storage medium (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-readable program code embodied therein or thereon.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus and systems according to examples of the disclosure. Although the above-described flowcharts illustrate a particular order of execution, the order of execution may vary from that depicted. Blocks described with respect to one flowchart may be combined with those of another flowchart. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by machine-readable instructions.

The machine-readable instructions may be executed by a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus, for example, to implement the functions as described in the specification and figures. In particular, a processor or processing device may execute machine-readable instructions. Thus, functional modules of the apparatus (such as model modification module 406, simulation module 408, or print instruction module 504) may be implemented by a processor executing machine-readable instructions stored in a memory or a processor operating in accordance with instructions embedded in logic circuitry. The term "processor" should be broadly interpreted to include a CPU, processing unit, ASIC, logic unit, or programmable gate array, etc. The methods and functional modules may all be executed by a single processor or may be divided among several processors.

Such machine-readable instructions may also be stored in a computer-readable storage device, which can direct a computer or other programmable data processing apparatus to function in a particular mode.

The machine-readable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause the computer or other programmable apparatus to perform a series of operations to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus implement the functions specified in the flowchart flow(s) and/or block(s) in the block diagram block or blocks.

Furthermore, the teachings herein may be implemented in the form of a computer software product stored in a storage medium and comprising a plurality of instructions for causing a computer device to implement the methods recited in the examples of the present disclosure.

Although the method, apparatus and related aspects have been described with reference to certain examples, various modifications, changes, omissions and substitutions can be made without departing from the spirit of the disclosure. Accordingly, it is intended that the method, apparatus, and related aspects be limited by the scope of the following claims and equivalents thereof. It should be noted that the above-mentioned examples illustrate rather than limit what is described herein, and that those skilled in the art will be able to design many alternative implementations without departing from the scope of the appended claims. Features described with respect to one example may be combined with features of another example.

The word "comprising" does not exclude the presence of elements other than those listed in a claim, the word "a" or "an" does not exclude a plurality, and a single processor or other unit may fulfill the functions of several units recited in the claims.

Features of any dependent claim may be combined with features of any of the independent or other dependent claims.

Claims

1. A method for generating an object, comprising:

receiving, by a processor, object model data representing an object to be generated by an additive manufacturing apparatus by depositing and fusing build material layer by layer within a manufacturing chamber;

Performing, by the processor, a simulated object generation operation for each of a plurality of different geometric compensation models, each geometric compensation model specifying geometric compensation to be applied to the object model data to compensate for deformations during cooling of the object after additive manufacturing of the object;

For each of the different geometric compensation models, generating, by the processor, a predicted attribute of the object based on the simulated object generating operation;

selecting one of the different geometric compensation models based on the simulated object generation operation;

modifying, by the processor, the object model data using the different geometric compensation model that has been selected, and

The additive manufacturing apparatus is caused to generate the object using the modified object model data by the processor.

2. The method of claim 1, wherein the predicted attribute comprises an expected object size of the object.

3. The method of claim 1, wherein the predicted attribute comprises an indication of a proportional deviation of an object size of the object based on a magnitude of the object size.

4. The method of claim 1, further comprising displaying predicted attributes associated with each of a plurality of different object model modifications based on different selected geometric compensation models and providing for selection between the selected geometric compensation models.

5. The method of claim 1, wherein the different geometric compensation models include different offset parameters and different scaling parameters.

6. The method of claim 1, wherein each of the different geometric compensation models is related to an expected location of an object to be generated within a manufacturing chamber.

7. A system for generating an object, comprising:

Processor, and

A memory for storing

Object model data representing an object generated by an additive manufacturing apparatus by depositing and fusing build material layer by layer within a manufacturing chamber, a plurality of different geometric compensation models, each geometric compensation model specifying geometric compensation to be applied to the object model data to compensate for deformation of the object during cooling after the additive manufacturing of the object, and

Program code, and

Wherein the program code is executable by the processor to:

Simulating an object generation operation for each of the plurality of different geometric compensation models; for each of the different geometric compensation models, generating an operation based on the modeled object to display a predicted attribute of the object;

modifying the object model data using the different geometric compensation model that has been selected, and

8. The system of claim 7, wherein the program code is executable by the processor to further determine print instructions for generating an object from the modified object model data.

9. The system of claim 8, wherein the printing instructions are used to cause the additive manufacturing device to generate the object using the modified object model data.

10. A non-transitory machine-readable medium comprising instructions that, when executed by a processor, cause the processor to:

Receiving object model data representing an object to be generated by an additive manufacturing apparatus by depositing and fusing build material layer by layer within a manufacturing chamber;

Simulating an object generation operation for each of a plurality of different geometric compensation models, each geometric compensation model specifying geometric compensation to be applied to the object model data to compensate for deformations during cooling of the object after additive manufacturing of the object;

Causing the additive manufacturing apparatus to generate the object using the modified object model data.

11. The non-transitory machine readable medium of claim 10, wherein selecting one of the different geometric compensation models comprises accepting a selection of one of the different geometric compensation models.